interfaces.niftyreg.regutils

RegAverage

Link to code

Wraps command reg_average

Interface for executable reg_average from NiftyReg platform.

Compute average matrix or image from a list of matrices or image. The tool can be use to resample images given input transformation parametrisation as well as to demean transformations in Euclidean or log-Euclidean space.

This interface is different than the others in the way that the options will be written in a command file that is given as a parameter.

Source code

Examples

>>> from nipype.interfaces import niftyreg
>>> node = niftyreg.RegAverage()
>>> one_file = 'im1.nii'
>>> two_file = 'im2.nii'
>>> three_file = 'im3.nii'
>>> node.inputs.avg_files = [one_file, two_file, three_file]
>>> node.cmdline  # doctest: +ELLIPSIS
'reg_average --cmd_file .../reg_average_cmd'

Inputs:

[Mandatory]

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
avg_files: (a list of items which are a file name)
        Averaging of images/affine transformations
        flag: -avg %s, position: 1
        mutually_exclusive: avg_lts_files, avg_ref_file, demean1_ref_file,
         demean2_ref_file, demean3_ref_file, warp_files
avg_lts_files: (a list of items which are a file name)
        Robust average of affine transformations
        flag: -avg_lts %s, position: 1
        mutually_exclusive: avg_files, avg_ref_file, demean1_ref_file,
         demean2_ref_file, demean3_ref_file, warp_files
avg_ref_file: (a file name)
        All input images are resampled into the space of <reference image>
        and averaged. A cubic spline interpolation scheme is used for
        resampling
        flag: -avg_tran %s, position: 1
        mutually_exclusive: avg_files, avg_lts_files, demean1_ref_file,
         demean2_ref_file, demean3_ref_file
        requires: warp_files
demean1_ref_file: (a file name)
        Average images and demean average image that have affine
        transformations to a common space
        flag: -demean1 %s, position: 1
        mutually_exclusive: avg_files, avg_lts_files, avg_ref_file,
         demean2_ref_file, demean3_ref_file
        requires: warp_files
demean2_ref_file: (a file name)
        Average images and demean average image that have non-rigid
        transformations to a common space
        flag: -demean2 %s, position: 1
        mutually_exclusive: avg_files, avg_lts_files, avg_ref_file,
         demean1_ref_file, demean3_ref_file
        requires: warp_files
demean3_ref_file: (a file name)
        Average images and demean average image that have linear and non-
        rigid transformations to a common space
        flag: -demean3 %s, position: 1
        mutually_exclusive: avg_files, avg_lts_files, avg_ref_file,
         demean1_ref_file, demean2_ref_file
        requires: warp_files
environ: (a dictionary with keys which are a bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
omp_core_val: (an integer (int or long), nipype default value: 1)
        Number of openmp thread to use
        flag: -omp %i
out_file: (a file name)
        Output file name
        flag: %s, position: 0
warp_files: (a list of items which are a file name)
        transformation files and floating image pairs/triplets to the
        reference space
        flag: %s, position: -1
        mutually_exclusive: avg_files, avg_lts_files

Outputs:

out_file: (a file name)
        Output file name

RegJacobian

Link to code

Wraps command reg_jacobian

Interface for executable reg_resample from NiftyReg platform.

Tool to generate Jacobian determinant maps from transformation parametrisation generated by reg_f3d

Source code

Examples

>>> from nipype.interfaces import niftyreg
>>> node = niftyreg.RegJacobian()
>>> node.inputs.ref_file = 'im1.nii'
>>> node.inputs.trans_file = 'warpfield.nii'
>>> node.inputs.omp_core_val = 4
>>> node.cmdline
'reg_jacobian -omp 4 -ref im1.nii -trans warpfield.nii -jac warpfield_jac.nii.gz'

Inputs:

[Mandatory]
trans_file: (an existing file name)
        The input non-rigid transformation
        flag: -trans %s

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
omp_core_val: (an integer (int or long), nipype default value: 1)
        Number of openmp thread to use
        flag: -omp %i
out_file: (a file name)
        The output jacobian determinant file name
        flag: %s, position: -1
ref_file: (an existing file name)
        Reference/target file (required if specifying CPP transformations.
        flag: -ref %s
type: ('jac' or 'jacL' or 'jacM', nipype default value: jac)
        Type of jacobian outcome
        flag: -%s, position: -2

Outputs:

out_file: (a file name)
        The output file

RegMeasure

Link to code

Wraps command reg_measure

Interface for executable reg_measure from NiftyReg platform.

Given two input images, compute the specified measure(s) of similarity

Source code

Examples

>>> from nipype.interfaces import niftyreg
>>> node = niftyreg.RegMeasure()
>>> node.inputs.ref_file = 'im1.nii'
>>> node.inputs.flo_file = 'im2.nii'
>>> node.inputs.measure_type = 'lncc'
>>> node.inputs.omp_core_val = 4
>>> node.cmdline
'reg_measure -flo im2.nii -lncc -omp 4 -out im2_lncc.txt -ref im1.nii'

Inputs:

[Mandatory]
flo_file: (an existing file name)
        The input floating/source image
        flag: -flo %s
measure_type: ('ncc' or 'lncc' or 'nmi' or 'ssd')
        Measure of similarity to compute
        flag: -%s
ref_file: (an existing file name)
        The input reference/target image
        flag: -ref %s

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
omp_core_val: (an integer (int or long), nipype default value: 1)
        Number of openmp thread to use
        flag: -omp %i
out_file: (a file name)
        The output text file containing the measure
        flag: -out %s

Outputs:

out_file: (a file name)
        The output text file containing the measure

RegResample

Link to code

Wraps command reg_resample

Interface for executable reg_resample from NiftyReg platform.

Tool to resample floating image in the space of a defined reference image given a transformation parametrisation generated by reg_aladin, reg_f3d or reg_transform

Source code

Examples

>>> from nipype.interfaces import niftyreg
>>> node = niftyreg.RegResample()
>>> node.inputs.ref_file = 'im1.nii'
>>> node.inputs.flo_file = 'im2.nii'
>>> node.inputs.trans_file = 'warpfield.nii'
>>> node.inputs.inter_val = 'LIN'
>>> node.inputs.omp_core_val = 4
>>> node.cmdline
'reg_resample -flo im2.nii -inter 1 -omp 4 -ref im1.nii -trans warpfield.nii -res im2_res.nii.gz'

Inputs:

[Mandatory]
flo_file: (an existing file name)
        The input floating/source image
        flag: -flo %s
ref_file: (an existing file name)
        The input reference/target image
        flag: -ref %s

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
inter_val: ('NN' or 'LIN' or 'CUB' or 'SINC')
        Interpolation type
        flag: -inter %d
omp_core_val: (an integer (int or long), nipype default value: 1)
        Number of openmp thread to use
        flag: -omp %i
out_file: (a file name)
        The output filename of the transformed image
        flag: %s, position: -1
pad_val: (a float)
        Padding value
        flag: -pad %f
psf_alg: (0 or 1)
        Minimise the matrix metric (0) or the determinant (1) when
        estimating the PSF [0]
        flag: -psf_alg %d
psf_flag: (a boolean)
        Perform the resampling in two steps to resample an image to a lower
        resolution
        flag: -psf
tensor_flag: (a boolean)
        Resample Tensor Map
        flag: -tensor
trans_file: (an existing file name)
        The input transformation file
        flag: -trans %s
type: ('res' or 'blank', nipype default value: res)
        Type of output
        flag: -%s, position: -2
verbosity_off_flag: (a boolean)
        Turn off verbose output
        flag: -voff

Outputs:

out_file: (a file name)
        The output filename of the transformed image

RegTools

Link to code

Wraps command reg_tools

Interface for executable reg_tools from NiftyReg platform.

Tool delivering various actions related to registration such as resampling the input image to a chosen resolution or remove the nan and inf in the input image by a specified value.

Source code

Examples

>>> from nipype.interfaces import niftyreg
>>> node = niftyreg.RegTools()
>>> node.inputs.in_file = 'im1.nii'
>>> node.inputs.mul_val = 4
>>> node.inputs.omp_core_val = 4
>>> node.cmdline
'reg_tools -in im1.nii -mul 4.0 -omp 4 -out im1_tools.nii.gz'

Inputs:

[Mandatory]
in_file: (an existing file name)
        The input image file path
        flag: -in %s

[Optional]
add_val: (a float or an existing file name)
        Add to the input image or value
        flag: -add %s
args: (a unicode string)
        Additional parameters to the command
        flag: %s
bin_flag: (a boolean)
        Binarise the input image
        flag: -bin
chg_res_val: (a tuple of the form: (a float, a float, a float))
        Change the resolution of the input image
        flag: -chgres %f %f %f
div_val: (a float or an existing file name)
        Divide the input by image or value
        flag: -div %s
down_flag: (a boolean)
        Downsample the image by a factor of 2
        flag: -down
environ: (a dictionary with keys which are a bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
inter_val: ('NN' or 'LIN' or 'CUB' or 'SINC')
        Interpolation order to use to warp the floating image
        flag: -interp %d
iso_flag: (a boolean)
        Make output image isotropic
        flag: -iso
mask_file: (an existing file name)
        Values outside the mask are set to NaN
        flag: -nan %s
mul_val: (a float or an existing file name)
        Multiply the input by image or value
        flag: -mul %s
noscl_flag: (a boolean)
        Set scale, slope to 0 and 1
        flag: -noscl
omp_core_val: (an integer (int or long), nipype default value: 1)
        Number of openmp thread to use
        flag: -omp %i
out_file: (a file name)
        The output file name
        flag: -out %s
rms_val: (an existing file name)
        Compute the mean RMS between the images
        flag: -rms %s
smo_g_val: (a tuple of the form: (a float, a float, a float))
        Smooth the input image using a Gaussian kernel
        flag: -smoG %f %f %f
smo_s_val: (a tuple of the form: (a float, a float, a float))
        Smooth the input image using a cubic spline kernel
        flag: -smoS %f %f %f
sub_val: (a float or an existing file name)
        Add to the input image or value
        flag: -sub %s
thr_val: (a float)
        Binarise the input image with the given threshold
        flag: -thr %f

Outputs:

out_file: (an existing file name)
        The output file

RegTransform

Link to code

Wraps command reg_transform

Interface for executable reg_transform from NiftyReg platform.

Tools to convert transformation parametrisation from one type to another as well as to compose, inverse or half transformations.

Source code

Examples

>>> from nipype.interfaces import niftyreg
>>> node = niftyreg.RegTransform()
>>> node.inputs.def_input = 'warpfield.nii'
>>> node.inputs.omp_core_val = 4
>>> node.cmdline  # doctest: +ELLIPSIS
'reg_transform -omp 4 -def warpfield.nii .../warpfield_trans.nii.gz'

Inputs:

[Mandatory]

[Optional]
aff_2_rig_input: (an existing file name)
        Extract the rigid component from affine transformation
        flag: -aff2rig %s, position: -2
        mutually_exclusive: def_input, disp_input, flow_input, comp_input,
         upd_s_form_input, inv_aff_input, inv_nrr_input, half_input,
         make_aff_input, flirt_2_nr_input
args: (a unicode string)
        Additional parameters to the command
        flag: %s
comp_input: (an existing file name)
        compose two transformations
        flag: -comp %s, position: -3
        mutually_exclusive: def_input, disp_input, flow_input,
         upd_s_form_input, inv_aff_input, inv_nrr_input, half_input,
         make_aff_input, aff_2_rig_input, flirt_2_nr_input
        requires: comp_input2
comp_input2: (an existing file name)
        compose two transformations
        flag: %s, position: -2
def_input: (an existing file name)
        Compute deformation field from transformation
        flag: -def %s, position: -2
        mutually_exclusive: disp_input, flow_input, comp_input,
         upd_s_form_input, inv_aff_input, inv_nrr_input, half_input,
         make_aff_input, aff_2_rig_input, flirt_2_nr_input
disp_input: (an existing file name)
        Compute displacement field from transformation
        flag: -disp %s, position: -2
        mutually_exclusive: def_input, flow_input, comp_input,
         upd_s_form_input, inv_aff_input, inv_nrr_input, half_input,
         make_aff_input, aff_2_rig_input, flirt_2_nr_input
environ: (a dictionary with keys which are a bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
flirt_2_nr_input: (a tuple of the form: (an existing file name, an
         existing file name, an existing file name))
        Convert a FLIRT affine transformation to niftyreg affine
        transformation
        flag: -flirtAff2NR %s %s %s, position: -2
        mutually_exclusive: def_input, disp_input, flow_input, comp_input,
         upd_s_form_input, inv_aff_input, inv_nrr_input, half_input,
         make_aff_input, aff_2_rig_input
flow_input: (an existing file name)
        Compute flow field from spline SVF
        flag: -flow %s, position: -2
        mutually_exclusive: def_input, disp_input, comp_input,
         upd_s_form_input, inv_aff_input, inv_nrr_input, half_input,
         make_aff_input, aff_2_rig_input, flirt_2_nr_input
half_input: (an existing file name)
        Half way to the input transformation
        flag: -half %s, position: -2
        mutually_exclusive: def_input, disp_input, flow_input, comp_input,
         upd_s_form_input, inv_aff_input, inv_nrr_input, make_aff_input,
         aff_2_rig_input, flirt_2_nr_input
inv_aff_input: (an existing file name)
        Invert an affine transformation
        flag: -invAff %s, position: -2
        mutually_exclusive: def_input, disp_input, flow_input, comp_input,
         upd_s_form_input, inv_nrr_input, half_input, make_aff_input,
         aff_2_rig_input, flirt_2_nr_input
inv_nrr_input: (a tuple of the form: (an existing file name, an
         existing file name))
        Invert a non-linear transformation
        flag: -invNrr %s %s, position: -2
        mutually_exclusive: def_input, disp_input, flow_input, comp_input,
         upd_s_form_input, inv_aff_input, half_input, make_aff_input,
         aff_2_rig_input, flirt_2_nr_input
make_aff_input: (a tuple of the form: (a float, a float, a float, a
         float, a float, a float, a float, a float, a float, a float, a
         float, a float))
        Make an affine transformation matrix
        flag: -makeAff %f %f %f %f %f %f %f %f %f %f %f %f, position: -2
        mutually_exclusive: def_input, disp_input, flow_input, comp_input,
         upd_s_form_input, inv_aff_input, inv_nrr_input, half_input,
         aff_2_rig_input, flirt_2_nr_input
omp_core_val: (an integer (int or long), nipype default value: 1)
        Number of openmp thread to use
        flag: -omp %i
out_file: (a file name)
        transformation file to write
        flag: %s, position: -1
ref1_file: (an existing file name)
        The input reference/target image
        flag: -ref %s, position: 0
ref2_file: (an existing file name)
        The input second reference/target image
        flag: -ref2 %s, position: 1
        requires: ref1_file
upd_s_form_input: (an existing file name)
        Update s-form using the affine transformation
        flag: -updSform %s, position: -3
        mutually_exclusive: def_input, disp_input, flow_input, comp_input,
         inv_aff_input, inv_nrr_input, half_input, make_aff_input,
         aff_2_rig_input, flirt_2_nr_input
        requires: upd_s_form_input2
upd_s_form_input2: (an existing file name)
        Update s-form using the affine transformation
        flag: %s, position: -2
        requires: upd_s_form_input

Outputs:

out_file: (a file name)
        Output File (transformation in any format)